def extendChainsInSentence(self, sentence, foundChains):
''' Rakendab meetodit self.extendChainsInClause() antud lause igal osalausel.
'''
# 1) Preprocessing
clauses = getClausesByClauseIDs(sentence)
# 2) Extend verb chains in each clause
allDetectedVerbChains = []
for clauseID in clauses:
clause = clauses[clauseID]
self.extendChainsInClause(clause, clauseID, foundChains)
def extendChainsInSentence( self, sentence, foundChains ):
''' Rakendab meetodit self.extendChainsInClause() antud lause igal osalausel.
'''
# 1) Preprocessing
clauses = getClausesByClauseIDs( sentence )
# 2) Extend verb chains in each clause
allDetectedVerbChains = []
for clauseID in clauses:
clause = clauses[clauseID]
self.extendChainsInClause(clause, clauseID, foundChains)
def detectVerbChainsFromSent( self, sentence, **kwargs):
''' Detect verb chains from given sentence.
Parameters
----------
sentence: list of dict
A list of sentence words, each word in form of a dictionary containing
morphological analysis and clause boundary annotations (must have CLAUSE_IDX);
Keyword parameters
------------------
expand2ndTime: boolean
If True, regular verb chains (chains not ending with 'olema') are expanded twice.
(default: False)
breakOnPunctuation: boolean
If True, expansion of regular verb chains will be broken in case of intervening punctuation.
(default: False)
removeSingleAraEi: boolean
if True, verb chains consisting of a single word, 'ära' or 'ei', will be removed.
(default: True)
removeOverlapping: boolean
If True, overlapping verb chains will be removed.
(default: True)
Returns
-------
list of dict
List of detected verb chains, each verb chain has following attributes (keys):
PHRASE -- list of int : indexes pointing to elements in sentence that belong
to the chain;
PATTERN -- list of str : for each word in phrase, marks whether it is 'ega', 'ei',
'ära', 'pole', 'ole', '&' (conjunction: ja/ning/ega/või)
'verb' (verb different than 'ole') or 'nom/adv';
ANALYSIS_IDS -- list of (list of int) : for each word in phrase, points to index(es) of
morphological analyses that correspond to words
in the verb chains;
ROOTS -- list of str : for each word in phrase, lists its corresponding ROOT
value from the morphological analysis; e.g. for the verb
chain 'püüab kodeerida', the ROOT will be ['püüd',
'kodeeri'];
MORPH -- list of str : for each word in phrase, lists its part-of-speech value
and morphological form (in one string, separated by '_',
and multiple variants of the pos/form separated by '/');
e.g. for the verb chain 'on tulnud', the MORPH value
will be ['V_vad/b', 'V_nud'];
OTHER_VERBS -- bool : whether there are other verbs in the context, potentially being
part of the verb chain; if this is True, it is uncertain whether
the chain is complete or not;
POLARITY -- 'POS', 'NEG' or '??' : grammatical polarity of the verb chain; Negative
polarity indicates that the verb phrase begins
with 'ei', 'ega', 'ära' or 'pole';
TENSE -- tense of the main verb: 'present', 'imperfect', 'perfect',
'pluperfect', 'past', '??';
MOOD -- mood of the main verb: 'indic', 'imper', 'condit', 'quotat', '??';
VOICE -- voice of the main verb: 'personal', 'impersonal', '??';
'''
# 0) Parse given arguments
expand2ndTime = False
removeOverlapping = True
removeSingleAraEi = True
breakOnPunctuation = False
for argName, argVal in kwargs.items():
if argName == 'expand2ndTime':
expand2ndTime = bool(argVal)
elif argName == 'removeOverlapping':
removeOverlapping = bool(argVal)
elif argName == 'removeSingleAraEi':
removeSingleAraEi = bool(argVal)
elif argName == 'breakOnPunctuation':
breakOnPunctuation = bool(argVal)
else:
raise Exception(' Unsupported argument given: '+argName)
# 1) Preprocessing
sentence = addWordIDs( sentence )
clauses = getClausesByClauseIDs( sentence )
# 2) Extract predicate-centric verb chains within each clause
allDetectedVerbChains = []
for clauseID in clauses:
clause = clauses[clauseID]
# 2.1) Extract predicate-centric verb chains within each clause
detectedBasicChains = _extractBasicPredicateFromClause(clause, clauseID)
allDetectedVerbChains.extend( detectedBasicChains )
# 2.2) Extract 'saama' + 'tud' verb phrases (typically rare)
_expandSaamaWithTud( clause, clauseID, allDetectedVerbChains )
# 2.3) Extend 'olema' chains with 'nud/tud/mas/mata' verbs (if possible)
_expandOlemaVerbChains( clause, clauseID, allDetectedVerbChains )
# 2.4) Expand non-olema verb chains inside the clause where possible (verb+verb chains)
_expandVerbChainsBySubcat( clause, clauseID, allDetectedVerbChains, self.verbInfSubcatLexicon, False, breakOnPunctuation)
# 2.5) Determine for which verb chains the context should be clear
# (no additional verbs can be added to the phrase)
_determineVerbChainContextualAmbiguity( clause, clauseID, allDetectedVerbChains)
# 2.6) Expand non-olema verb chains inside the clause 2nd time (verb+verb+verb chains)
# (Note that while verb+verb+verb+verb+... chains are also possible, three verbs
# seems to be a critical length: longer chains are rare and thus making longer
# chains will likely lead to errors);
if expand2ndTime:
_expandVerbChainsBySubcat( clause, clauseID, allDetectedVerbChains, self.verbInfSubcatLexicon, False, breakOnPunctuation)
# 3) Extract 'ega' negations (considering the whole sentence context)
expandableEgaFound = _extractEgaNegFromSent( sentence, clauses, allDetectedVerbChains )
if expandableEgaFound:
for clauseID in clauses:
clause = clauses[clauseID]
# 3.1) Expand non-olema 'ega' verb chains inside the clause, if possible;
_expandVerbChainsBySubcat( clause, clauseID, allDetectedVerbChains, self.verbInfSubcatLexicon, False, breakOnPunctuation)
#_debugPrint(' | '+getJsonAsTextString(sentence, markTokens = [ verbObj[PHRASE] for verbObj in allDetectedVerbChains ]))
# 4) Extend chains with nom/adv + Vinf relations
if self.verbNomAdvVinfExtender:
addGrammaticalFeatsAndRoots( sentence, allDetectedVerbChains )
for clauseID in clauses:
clause = clauses[clauseID]
expansionPerformed = \
self.verbNomAdvVinfExtender.extendChainsInClause( clause, clauseID, allDetectedVerbChains )
if expansionPerformed:
_determineVerbChainContextualAmbiguity( clause, clauseID, allDetectedVerbChains)
# ) Remove redundant and overlapping verb phrases
removeRedundantVerbChains( allDetectedVerbChains, removeOverlapping = removeOverlapping, removeSingleAraAndEi = removeSingleAraEi )
# ) Add grammatical features (in the end)
addGrammaticalFeatsAndRoots( sentence, allDetectedVerbChains )
return allDetectedVerbChains
def detectVerbChainsFromSent(self, sentence, **kwargs):
''' Detect verb chains from given sentence.
Parameters
----------
sentence: list of dict
A list of sentence words, each word in form of a dictionary containing
morphological analysis and clause boundary annotations (must have CLAUSE_IDX);
Keyword parameters
------------------
expand2ndTime: boolean
If True, regular verb chains (chains not ending with 'olema') are expanded twice.
(default: False)
breakOnPunctuation: boolean
If True, expansion of regular verb chains will be broken in case of intervening punctuation.
(default: False)
removeSingleAraEi: boolean
if True, verb chains consisting of a single word, 'ära' or 'ei', will be removed.
(default: True)
removeOverlapping: boolean
If True, overlapping verb chains will be removed.
(default: True)
Returns
-------
list of dict
List of detected verb chains, each verb chain has following attributes (keys):
PHRASE -- list of int : indexes pointing to elements in sentence that belong
to the chain;
PATTERN -- list of str : for each word in phrase, marks whether it is 'ega', 'ei',
'ära', 'pole', 'ole', '&' (conjunction: ja/ning/ega/või)
'verb' (verb different than 'ole') or 'nom/adv';
ANALYSIS_IDS -- list of (list of int) : for each word in phrase, points to index(es) of
morphological analyses that correspond to words
in the verb chains;
ROOTS -- list of str : for each word in phrase, lists its corresponding ROOT
value from the morphological analysis; e.g. for the verb
chain 'püüab kodeerida', the ROOT will be ['püüd',
'kodeeri'];
MORPH -- list of str : for each word in phrase, lists its part-of-speech value
and morphological form (in one string, separated by '_',
and multiple variants of the pos/form separated by '/');
e.g. for the verb chain 'on tulnud', the MORPH value
will be ['V_vad/b', 'V_nud'];
OTHER_VERBS -- bool : whether there are other verbs in the context, potentially being
part of the verb chain; if this is True, it is uncertain whether
the chain is complete or not;
POLARITY -- 'POS', 'NEG' or '??' : grammatical polarity of the verb chain; Negative
polarity indicates that the verb phrase begins
with 'ei', 'ega', 'ära' or 'pole';
TENSE -- tense of the main verb: 'present', 'imperfect', 'perfect',
'pluperfect', 'past', '??';
MOOD -- mood of the main verb: 'indic', 'imper', 'condit', 'quotat', '??';
VOICE -- voice of the main verb: 'personal', 'impersonal', '??';
'''
# 0) Parse given arguments
expand2ndTime = False
removeOverlapping = True
removeSingleAraEi = True
breakOnPunctuation = False
for argName, argVal in kwargs.items():
if argName == 'expand2ndTime':
expand2ndTime = bool(argVal)
elif argName == 'removeOverlapping':
removeOverlapping = bool(argVal)
elif argName == 'removeSingleAraEi':
removeSingleAraEi = bool(argVal)
elif argName == 'breakOnPunctuation':
breakOnPunctuation = bool(argVal)
else:
raise Exception(' Unsupported argument given: ' + argName)
# 1) Preprocessing
sentence = addWordIDs(sentence)
clauses = getClausesByClauseIDs(sentence)
# 2) Extract predicate-centric verb chains within each clause
allDetectedVerbChains = []
for clauseID in clauses:
clause = clauses[clauseID]
# 2.1) Extract predicate-centric verb chains within each clause
detectedBasicChains = _extractBasicPredicateFromClause(
clause, clauseID)
allDetectedVerbChains.extend(detectedBasicChains)
# 2.2) Extract 'saama' + 'tud' verb phrases (typically rare)
_expandSaamaWithTud(clause, clauseID, allDetectedVerbChains)
# 2.3) Extend 'olema' chains with 'nud/tud/mas/mata' verbs (if possible)
_expandOlemaVerbChains(clause, clauseID, allDetectedVerbChains)
# 2.4) Expand non-olema verb chains inside the clause where possible (verb+verb chains)
_expandVerbChainsBySubcat(clause, clauseID, allDetectedVerbChains,
self.verbInfSubcatLexicon, False,
breakOnPunctuation)
# 2.5) Determine for which verb chains the context should be clear
# (no additional verbs can be added to the phrase)
_determineVerbChainContextualAmbiguity(clause, clauseID,
allDetectedVerbChains)
# 2.6) Expand non-olema verb chains inside the clause 2nd time (verb+verb+verb chains)
# (Note that while verb+verb+verb+verb+... chains are also possible, three verbs
# seems to be a critical length: longer chains are rare and thus making longer
# chains will likely lead to errors);
if expand2ndTime:
_expandVerbChainsBySubcat(clause, clauseID,
allDetectedVerbChains,
self.verbInfSubcatLexicon, False,
breakOnPunctuation)
# 3) Extract 'ega' negations (considering the whole sentence context)
expandableEgaFound = _extractEgaNegFromSent(sentence, clauses,
allDetectedVerbChains)
if expandableEgaFound:
for clauseID in clauses:
clause = clauses[clauseID]
# 3.1) Expand non-olema 'ega' verb chains inside the clause, if possible;
_expandVerbChainsBySubcat(clause, clauseID,
allDetectedVerbChains,
self.verbInfSubcatLexicon, False,
breakOnPunctuation)
#_debugPrint(' | '+getJsonAsTextString(sentence, markTokens = [ verbObj[PHRASE] for verbObj in allDetectedVerbChains ]))
# 4) Extend chains with nom/adv + Vinf relations
if self.verbNomAdvVinfExtender:
addGrammaticalFeatsAndRoots(sentence, allDetectedVerbChains)
for clauseID in clauses:
clause = clauses[clauseID]
expansionPerformed = \
self.verbNomAdvVinfExtender.extendChainsInClause( clause, clauseID, allDetectedVerbChains )
if expansionPerformed:
_determineVerbChainContextualAmbiguity(
clause, clauseID, allDetectedVerbChains)
# ) Remove redundant and overlapping verb phrases
removeRedundantVerbChains(allDetectedVerbChains,
removeOverlapping=removeOverlapping,
removeSingleAraAndEi=removeSingleAraEi)
# ) Add grammatical features (in the end)
addGrammaticalFeatsAndRoots(sentence, allDetectedVerbChains)
return allDetectedVerbChains
